---
license: apache-2.0
base_model:
- Qwen/Qwen-Image-Edit
language:
- en
- zh
library_name: diffusers
pipeline_tag: image-to-image
datasets:
- OPPOer/X2Edit-Dataset
---
<div align="center">
  <h1>Qwen-Image-Edit-Pruning</h1>
<a href='https://github.com/OPPO-Mente-Lab/Qwen-Image-Pruning'><img src="https://img.shields.io/badge/GitHub-OPPOer-blue.svg?logo=github" alt="GitHub"></a>
</div>

## Update
- 2025/10/09: We release **[Qwen-Image-Edit-2509-Pruning-13B-4steps](https://huggingface.co/OPPOer/Qwen-Image-Edit-2509-Pruning)**
- 2025/09/29: We release **[Qwen-Image-Edit-2509-Pruning-14B](https://huggingface.co/OPPOer/Qwen-Image-Edit-2509-Pruning)**
- 2025/09/28: We release **[Qwen-Image-Edit-Pruning-13B-4steps](https://huggingface.co/OPPOer/Qwen-Image-Edit-Pruning)** 


## Introduction
This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters. The pruned version will continue to be iterated upon. Please stay tuned.

<div align="center">
  <img src="bench.png">
</div>

## Quick Start

Install the latest version of diffusers and pytorch
```
pip install torch
pip install git+https://github.com/huggingface/diffusers
```

### Qwen-Image-Edit-13B Inference
```python
from diffusers import QwenImageEditPipeline
import os
from PIL import Image
import time
import torch
model_name = "OPPOer/Qwen-Image-Edit-Pruning/Qwen-Image-Edit-13B-4steps"
pipe = QwenImageEditPipeline.from_pretrained(model_name, torch_dtype=torch.bfloat16)
pipe = pipe.to('cuda')

subject_img = Image.open('input.jpg').convert('RGB')
prompt = '改为数字插画风格'
t1 = time.time()
inputs = {
    "image": subject_img,
    "prompt": prompt,
    "generator": torch.manual_seed(42),
    "true_cfg_scale": 1,
    "num_inference_steps": 4,
}
with torch.inference_mode():
    output = pipe(**inputs)
    output_image = output.images[0]
    output_image.save('output.jpg')
```